Colon Cancer

Exploring Real-Time Tracking of Vocal Fold Polyps in Video-Stroboscopy Using Deep Learning.

OBJECTIVE: To develop and evaluate a deep learning object detection system for identifying vocal fold polyps in stroboscopic video frames using You Only Look Once (YOLO), and to assess the added benefit of temporal tracking on detection performance. METHODS: A retrospective dataset of 12,742 annotated frames from 55 laryngoscopy video recordings was annotated with bounding boxes identifying vocal ...

Single‑center weakly supervised deep learning prediction of KRAS, NRAS, BRAF, and HER2 status in colorectal cancer from histopathology images using internal cross‑validation.

Overcoming Resistance in EGFR-Mutant Cancers: A Comprehensive Review of Inhibitor Evolution and SAR-Based Design.

The epidermal growth factor receptor (EGFR) is a key target in cancer therapy, mainly in non-small cell lung cancer (NSCLC). Though, the efficacy of E...

Impact of Artificial Intelligence for Detection of Precancerous Colonic Lesions in a Fecal Immunochemical Blood Test-Based Organized Screening Program in Italy: A Randomized Control Trial.

BACKGROUND: The fecal immunochemical test (FIT) is widely implemented as a first-line tool in organized colorectal cancer (CRC) screening programs, in...

State of the art in soft eversion robots for colonoscopy: a review.

This review explores the current state of eversion robotics in the context of colonoscopy, given the need for less invasive, more patient-friendly scr...

Identification of diagnostic and prognostic biomarkers in lung adenocarcinoma through integrated bioinformatics analysis and real time PCR validation.

Lung cancer is the third most common cancer in the US with a 5-year survival rate of 17%. Non-small cell lung cancer, especially adenocarcinoma, preva...

Multi-omics analysis reveals features improving outcomes and immunotherapy resistance in MSS colorectal cancer.

In contrast to the MSI-H subtype, most microsatellite-stable (MSS) colorectal cancer (CRC) patients exhibit poor prognosis and limited responsiveness ...

Unraveling heterogeneity in LUAD via multi-omics integration: molecular classification and therapeutic implications.

Lung adenocarcinoma (LUAD) displays significant morphological and molecular heterogeneity both within and between tumors. This heterogeneity, coupled ...

Construction of a risk model based on exosome-related genes predict clinical prognosis and therapeutic response and revealing TIMP1 as a promising target in colorectal cancer.

Various exosome-derived proteins have been reported to play essential roles in regulating colorectal cancer progression and affecting the prognosis of...

Interpretable machine learning model for predicting 5-Year postoperative recurrence risk in patients with stage III colon cancer using preoperative laboratory tests: a two-centre study.

BACKGROUND: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide and displays significant heterogeneity. The aim of this ...

A multi-algorithm machine learning framework identifies FGD5, LRRC36, C8B, and MYOC as novel diagnostic biomarkers in lung adenocarcinoma.

18F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.

PURPOSE: Predicting treatment response in patients with colorectal cancer liver metastases (CRCLM) who have undergone transarterial radioembolization ...

A machine learning-based transcriptomic signature for predicting tumor recurrence after curative resection in T1 colorectal cancer: a retrospective multicenter cohort study (The Tw1CE trial).

BACKGROUND: T1 colorectal cancer (T1 CRC) is increasingly treated with curative-intent endoscopic resection, but tumor recurrence remains a critical f...

Artificial Intelligence Driven Virtual Screening and Molecular Docking Approaches Identified LIFR, BTG2, EPHX2, and PAK3 as Targets and BI-2536, AP-24534, and AZ-628 as Repurposed Drugs for PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal tumors worldwide, with limited effective treatments. Globally, the in...

Predicting and interpreting protein and phosphoprotein abundance from pan-cancer and single-cell transcriptomes.

Proteins that impact phenotype and disease are often approximated by RNA expression, which poorly infers protein abundance. We developed DeepGxP, a de...

Integrating artificial intelligence (AI) into colorectal cancer reporting.

Artificial intelligence (AI) and deep learning (DL) are transforming cancer research and clinical care, with histopathology playing a central role in ...

[Centennial evolution and innovative practices of functional protection in colorectal cancer surgery].

In the over-century-long history of colorectal tumor surgery, achieving organ function preservation has consistently been a central topic. Currently, ...

[Sphincter-preserving surgery for rectal cancer--From limits to perfection].

Sphincter-preserving therapy for low rectal cancer has always been a difficult and hot topic in colorectal cancer. Advanced understanding of the biolo...

[A new paradigm for precision diagnosis and treatment of colorectal cancer in the era of artificial intelligence--From foundation models to multi-agent collaboration].

The high heterogeneity of colorectal cancer poses significant challenges to clinical precision diagnosis and treatment. In recent years, artificial in...

[Precision surgical treatment for middle and low rectal cancer in the era of artificial intelligence].

Precise preoperative assessment and the development of individualized surgical strategies are critical determinants in minimizing operative risks and ...